With the popularization of communication technology, mobile devices are conveniently used to transfer and obtain information. The resulting communication network security issues are drawing more and more attention. This study is dedicated to the modeling and optimal control problems for the malware propagation between mobile devices, in which the patch-implantation and device repair are considered. A fractional network-based SVEIS (Susceptible-Vaccinated-Exposed-Infectious-Susceptible) model is developed and investigated. Different from the previous malware propagation models, the considered transmission mechanism not only includes imperfect patch-implantation and reinfection after repair, but also incorporates the impact of memory effect. The next-generation matrix method is employed to obtain a critical threshold, which denotes the mean number of newly infectious devices resulting from an infectious device in a fully susceptible mobile devices environment. The existence and stability of the steady state are analyzed correspondingly to facilitate the control of malware. The cases where multi-equilibria coexist are also given in detail. Moreover, the optimal control strategy with respect to patch-implantation and repair is designed to trade off the infectious device prevalence and control cost. The proposed model provides a benchmark for patch-implantation and repair rate in a connected mobile device system. Finally, numerical simulations are conducted to assess the model’s effectiveness while investigating the sensitivity of its parameters in the context of a Barabási-Albert (BA) scale-free network. The control effects of various strategies are evaluated through comparative experiments. And a comparison with two established models is conducted to validate the enhanced risk assessment capabilities of the proposed framework.

This article focuses on the resilient fixed-time stabilization of switched neural networks (SNNs) under impulsive deception attacks. A novel theorem for the fixed-time stability of impulsive systems is established by virtue of the comparison principle. Existing fixed-time stability theorems for impulsive systems assume that the impulsive strength is not greater than 1, while the proposed theorem removes this assumption. SNNs subjected to impulsive deception attacks are modeled as impulsive systems. Some sufficient criteria are derived to ensure the stabilization of SNNs in fixed time. The estimation of the upper bound for the settling time is also given. The influence of impulsive attacks on the convergence time is discussed. A numerical example and an application to Chua's circuit system are given to demonstrate the effectiveness of the theoretical results.

This article addresses the problem of dynamic event-triggered platooning control of automated vehicles over a vehicular ad-hoc network (VANET) subject to random vehicle-to-vehicle communication topologies. First, a novel dynamic event-triggered mechanism is developed to determine whether or not the sampled data packets of each vehicle should be released into the VANET for intervehicle cooperation. More specifically, the threshold parameter in the triggering condition is dynamically adjusted over time according to the vehicular data variations, the dynamic threshold updating laws, and the bandwidth occupancy indication. Second, a unified platooning control framework is established to account for various spacing policies, randomly switching communication topologies, unknown leader control input, and external disturbances. Then, a new scheduling and platooning control co-design approach is presented such that the controlled vehicular platoon can successfully track the leader vehicle under random communication topologies and different spacing policies, including constant spacing, constant time headway spacing, and variable time headway spacing, meanwhile maintaining efficient bandwidth-aware resource management. Finally, comparative studies are provided to substantiate the effectiveness and merits of the proposed co-design approach.

This article deals with the problem of secure distributed adaptive platooning control of automated vehicles over vehicular ad-hoc networks (VANETs) in the presence of intermittent denial-of-service (DoS) attacks. The platoon, which is wirelessly connected via directed vehicle-to-vehicle (V2V) communication, is composed of a group of following vehicles subject to unknown heterogeneous nonlinearities and external disturbance inputs, and a leading vehicle subject to unknown nonlinearity and external disturbance as well as an unknown control input. Under such a platoon setting, this article aims to accomplish secure distributed platoon formation tracking with the desired longitudinal spacing and the same velocities and accelerations guided by the leader regardless of the simultaneous presence of nonlinearities, uncertainties, and DoS attacks. First, a new logical data packet processor is developed on each vehicle to identify the intermittent DoS attacks via verifying the time-stamps of the received data packets. Then, a scalable distributed neural-network-based adaptive control design approach is proposed to achieve secure platooning control. It is proved that under the established design procedure, the vehicle state estimation errors and platoon tracking errors can be regulated to reside in small neighborhoods around zero. Finally, comparative simulation studies are provided to substantiate the effectiveness and merits of the proposed control design approach on maintaining the desired platooning performance and attack tolerance.

In this article, an event-triggered control problem for partial differential equation systems with pointwise measurements is investigated via relaxed Lyapunov–Krasovskii functionals. First, the Takagi–Sugeno fuzzy model is introduced to describe the nonlinear systems and a fuzzy event-triggered pointwise controller is proposed with pointwise measurements, which can make a tradeoff between the system’s performance and implementation complexity subject to limited transmission bandwidth. Second, some relaxed conditions are established to ensure the closed-loop system’s stability by using the Lyapunov method and inequality techniques. Finally, two simulation examples are provided to demonstrate the effectiveness and practicability of the designed controller.

This paper aims to investigate the event-based extended dissipative state estimation problem for memristor-based Markovian neural networks in the presence of hybrid time-varying delays and sensor nonlinearity. To tackle the effect caused by information latching, sudden interference and environmental variation, the Markov jump model is employed to describe the memristor-based neural network. Besides, an event-triggered scheme is introduced to economize the cost of communication. Then some novel conditions are presented, which guarantee that the augmented error system is stochastically stable with an extended dissipative performance. The existence criterion of the desired mode-dependent estimator is also obtained in terms of linear matrix inequalities. Finally, simulation results are provided to show the effectiveness of the proposed method.

This article is concerned with the distributed resilient estimation of a positive system over a sensor network. First, a heterogeneous sensor interaction framework, where each sensor is capable of sharing its local information of measurement as well as state estimate with its underlying neighbors via distinct interaction topologies, is proposed to account for different sensor communication capacities. During the information exchanges among the sensors, topological attacks are suitably modeled in such a way to incorporate the random and intermittent disruption of the heterogeneous sensor interaction topologies. Second, two sets of distributed resilient estimators are delicately constructed to cope with the resulting random denial of information exchanges within the specific repaired periods and compromised periods caused by the topological attacks. Third, the resilience performance analysis with a prescribedl1-gain attenuation level is carried out, and a linear programming approach is then developed to achieve the design of the desired distributed estimators. Finally, the effectiveness of the proposed design method is verified through a vehicle formation monitoring system.

This paper presents an auxiliary random series approach to model the effect of network induced problems, such as data losses and transmission delay subject to event-based communication scheme for nonlinear continuous time systems. T-S fuzzy model is employed to describe the nonlinear systems. In order to save the bandwidth and energy, we introduce the event-triggered mechanism to reduce the number of data for transmission and computation. Thus, it is necessary to consider the influence of data losses, data disorder, and transmission delay since the transmitted data packets become more important. Consequently, it is very complicated to analyze the performance of such networked system and one of the most difficult part, in the authors' opinion, is to construct the mathematical model of closed-loop systems. In this paper, we present an auxiliary random series approach to describe the data transmitted in the system, and therefore, the closed-loop systems can be obtained. Associated with a tailor-made Lyapunov-Krasovskii functional, the stability analysis is processed and a fuzzy controller is designed. Asynchronous membership functions are considered to obtain more relaxed stability conditions. To clarify the effectiveness of the proposed method, a cart-damper-spring system is employed for simulation.

This paper is concerned with secure 11-gain performance analysis and distributed finite-time filter design for a positive discrete-time linear system over a sensor network in the presence of deception attacks. A group of intercommunicating sensors is densely deployed to measure, gather, and process the output of the positive system. Each sensor is capable of sharing its measurement with its neighboring sensors in accordance with a prescribed network topology while suffering from random communication link failure. Meanwhile, the aggregated measurement on each sensor during network transmission is corrupted by stochastic deception attacks which compromise the sensor's measurement integrity. First, a unified sensor measurement transmission model is put forward to account for the simultaneous presence of deception attacks and various network-induced constraints. Second, delicate secure distributed filters are constructed by admitting the corrupted sensor measurement. Third, theoretical analysis on finite-time 11-gain boundedness of the filtering error system and design of desired positive filters are carried out. The solution to the filter gain parameters is characterized by a set of linear programming inequalities. Finally, the effectiveness of the obtained results is verified through the secure monitoring of power distribution in the smart grid.

This paper aims to design a dissipative fuzzy filter for a class of discrete-time nonlinear networked systems. In order to adopt the limited communication links, we employ an eventtriggered scheme to reduce the number of transmitted data in the network by preventing the unnecessary ones from releasing. Due to the digital channel, the data to be transmitted should be quantized and a logarithmic quantizer is employed. Then, when the part of released data is transmitted in the network, data losses is captured by a Bernoulli process. Consequently, the uncomplete data sequence is compensated by the buffer and then send to the filter. During this process, a new random series is developed to help constructing the filtering systems. Thus, a novel method is presented to guarantee the filter error system to be dissipative based on the T-S fuzzy model approach. Finally, an example concerned with Henon mapping system is provided to verify the validity of the proposed design method.

This paper is concerned with the state-feedback stabilization for singular Markovian jump systems with distributed delays. Firstly, delay-dependent admissibility conditions are obtained by employing a mode-dependent Lyapunov-Krasovskii functional and applying the delay-partitioning technique. Secondly, strict LMI-based conditions for solving the stabilization problem are presented, based on which the desired controller gains can be determined. Finally, a numerical example is given to illustrate the effectiveness of the proposed methods.

This paper is concerned with the problem of H∞state estimation for a class of discrete-time singular systems with mixed delays and sensors saturation. A comprehensive model of the state estimation error system is constructed based on the event-triggered scheme. In order to improve the efficiency in resource utilization and reduce the load of network communication, for each sensor, an event-detector is adopted to determine whether the measurement signals should be transmitted to the state estimator or not. By using the Lyapunov functional theory, a sufficient condition is obtained to guarantee the estimation error system is regular, casual, and stable with a prescribed H∞performance index. The criterion is formulated as a set of strict linear matrix inequalities (LMIs). The design method of estimator gain matrix is further proposed. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method.

This paper is concerned with the L1-gain filtering problem of positive linear continuous-time systems in which the signals are transmitted through event-triggered network communication channels. A network-based filter system model is proposed to estimate the variables of a positive system. The event-triggered communication scheme in a linear form is presented to cut down the amount of the data in transmission, and save the limited communication bandwidth, as well. By constructing an augmented filtering error system and using the linear Lyapunov method, a sufficient condition to ensure the existence of the L1-gain filter is derived. In addition, a linear programming approach to the filter design is proposed through which the filter parameters can be obtained. A numerical example is presented to illustrate the theoretical results.

This paper focuses on the event-triggered network-based synchronization problem for a class of complex networks with delayed couplings. The dynamic of each node in the considered networks is described by a neutral neural network. The remote master and the complex networks transmit signals mutually through the common communication channels, which include network-induced delays and stochastic fluctuations. An event-triggering scheme is proposed and utilized when the controller transfer signals to the complex networks. A state error system is formulated in which the event-triggering scheme is concerned with. By using Lyapunov method and some properties of Kronecker product, some synchronization criteria are derived to ensure the global mean-square exponential synchronization of state trajectories of the remote master and the coupled dynamic networks. The event-triggered controller design method is further proposed.

In this paper, we are concerned with the problem of stability analysis and stabilization control design for Takagi-Sugeno (T-S) fuzzy systems with probabilistic interval delay. By employing the information of probability distribution of the time delay, the original system is transformed into a T-S fuzzy model with stochastic parameter matrices. Based on the new type of T-S fuzzy model, the delay-distribution-dependent criteria for the mean-square exponential stability of the considered systems are derived by using the Lyapunov-Krasovskii functional method, parallel distributed compensation approach, and the convexity of some matrix equations. The solvability of the derived criteria depends not only on the size of the delay but also on the probability distribution of the delay taking values in some intervals. The revisions of the main criteria in this paper can also be used to deal with the case when only the information of variation range of the delay is considered. It is shown by practical examples that our method can lead to very less conservative results than those by other existing methods.

This brief is concerned with the analysis problem of global exponential stability in the mean square sense for a class of linear discrete-time recurrent neural networks (DRNNs) with stochastic delay. Different from the prior research works, the effects of both variation range and probability distribution of the time delay are involved in the proposed method. First, a modeling method is proposed by translating the probability distribution of the time delay into parameter matrices of the transformed DRNN model, where the delay is characterized by a stochastic binary distributed variable. Based on the new method, the global exponential stability in the mean square sense for the DRNNs with stochastic delay is investigated by using the Lyapunov-Krasovskii functional and exploiting some new analysis techniques. A numerical example is provided to show the effectiveness and the applicability of the proposed method.